Popular Libraries

Import the hmmlearn and joblib libraries.

from AlgorithmImports import *
from hmmlearn import hmm
import joblib

You need the joblib library to store models.

In the Initializeinitialize method, subscribe to some data so you can train the hmmlearn model and make predictions.

# Subscribe to security data and store symbol for referencing in the algorithm.
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol

In this example, assume the market has only 2 regimes and the market returns follow a Gaussian distribution. Therefore, create a 2-component Hidden Markov Model with Gaussian emissions, which is equivalent to a Gaussian mixture model with 2 means.

To build the model, call the GaussianHMM constructor with the number of components, a covariance type, and the number of iterations:

# Initialize GaussianHMM with 2 states for capturing patterns, full covariance to model complex relationships, and 100 iterations for sufficient training.
self.model = hmm.GaussianHMM(n_components=2, covariance_type="full", n_iter=100)

You can train the model at the beginning of your algorithm and you can periodically re-train it as the algorithm executes.

Warm Up Training Data

You need historical data to initially train the model at the start of your algorithm. To get the initial training data, in the Initializeinitialize method, make a history request.

# Initialize training data with a rolling window of size 252*2 days.
training_length = 252*2
self.training_data = RollingWindow[float](training_length)
history = self.history[TradeBar](self._symbol, training_length, Resolution.DAILY)
for trade_bar in history:
    self.training_data.add(trade_bar.close)

Define a Training Method

To train the model, define a method that fits the model with the training data.

# Prepare feature and label data for training by processing rolling window data to create time-series sequences for model training.
def get_features(self):
    training_df = np.array(list(self.training_data)[::-1])
    daily_pct_change = (np.roll(training_df, 1) - training_df) / training_df

    return daily_pct_change[1:].reshape(-1, 1)

def my_training_method(self):
    features = self.get_features()
    self.model.fit(features)

Set Training Schedule

To train the model at the beginning of your algorithm, in the Initializeinitialize method, call the Traintrain method.

# Train the model initially to provide a baseline for prediction and decision-making.
self.train(self.my_training_method)

To periodically re-train the model as your algorithm executes, in the Initializeinitialize method, call the Traintrain method as a Scheduled Event.

# Train the model every Sunday at 8:00 AM
self.train(self.date_rules.every(DayOfWeek.SUNDAY), self.time_rules.at(8, 0), self.my_training_method)

Update Training Data

To update the training data as the algorithm executes, in the OnDataon_data method, add the current close price to the RollingWindow that holds the training data.

# Add the latest bar to training data to ensure the model is trained with the most recent market data.
def on_data(self, slice: Slice) -> None:
    if self._symbol in slice.bars:
        self.training_data.add(slice.bars[self._symbol].close)

To predict the labels of new data, in the OnDataon_data method, get the most recent set of features and then call the predict method.

# Generate feature set and predict with the latest data for current market decisions.
new_feature = self.get_features()
prediction = self.model.predict(new_feature)
prediction = float(prediction[-1])

You can use the label prediction to place orders.

# Use label prediction to place orders based on forecasted market direction.
if prediction == 1:
    self.set_holdings(self._symbol, 1)
else:            
    self.liquidate(self._symbol)

Follow these steps to save hmmlearn models into the Object Store:

1. Set the key name you want to store the model under in the Object Store.

# Set the key to store the transformer and regressor models in Object Store for reuse across sessions.
model_key = "model.hmm"

2. Call the GetFilePathget_file_path method with the key.

# Get the file path to correctly save and access the model in Object Store.
file_name = self.object_store.get_file_path(model_key)

This method returns the file path where the model will be stored.

3. Call the dump method the file path.

# Serialize Python objects into a file to save the model's state for other runs.
joblib.dump(self.model, file_name)

If you dump the model using the joblib module before you save the model, you don't need to retrain the model.

You can load and trade with pre-trained hmmlearn models that you saved in the Object Store. To load a hmmlearn model from the Object Store, in the Initializeinitialize method, get the file path to the saved model and then call the load method.

# Load the hmmlearn model from Object Store to use its saved state and update with new data if needed.
def initialize(self) -> None:
    if self.object_store.contains_key(model_key):
        file_name = self.object_store.get_file_path(model_key)
        self.model = joblib.load(file_name)

The ContainsKeycontains_key method returns a boolean that represents if the model_key is in the Object Store. If the Object Store does not contain the model_key, save the model using the model_key before you proceed.